The current facial emotion recognition systems are evolved from the fundamental system—the Facial Action Coding System (FACS)—that helps the grouping of the facial changes based on their descriptions on the surface of the facial regions [1]}. The FACS has been later extended by Tian, Kande, and Cohn [2]} with the concept of Action Units, which allow the integration of facial landmarks (e.g. lips, eyes, and mouth) into the system to improve its performance in terms of the grouping of the emotions (e.g., happy, anger, and sad). The integration of the landmarks creates a significant amount of uncertainties while increasing computational complexity. Hence, Viola and Jones [3]} proposed an algorithm to increase the speed of computation by adapting the AdaBoost learning and cascade approach to speed up the computation along with a new concept called Integral Image that also allows faster computation in a real-time environment. In our approach, we tried to avoid the use of landmarks; however, the frequency domain techniques indirectly include the emotional frequencies emitted in the facial landmark regions.
